30 citations · 91 across the 44 of their papers we have counts for
5 papers · 1 filter
Learning Positional Embeddings for Coordinate-MLPs
Sameera Ramasinghe, Simon Lucey
We propose a novel method to enhance the performance of coordinate-MLPs by learning instance-specific positional embeddings. End-to-end optimization of positional embedding paramet…
Beyond Periodicity: Towards a Unifying Framework for Activations in Coordinate-MLPs
Sameera Ramasinghe, Simon Lucey
Coordinate-MLPs are emerging as an effective tool for modeling multidimensional continuous signals, overcoming many drawbacks associated with discrete grid-based approximations. Ho…
Enabling equivariance for arbitrary Lie groups
Lachlan Ewen MacDonald, Sameera Ramasinghe, Simon Lucey
Although provably robust to translational perturbations, convolutional neural networks (CNNs) are known to suffer from extreme performance degradation when presented at test time w…
Rethinking Positional Encoding
Jianqiao Zheng, Sameera Ramasinghe, Simon Lucey
It is well noted that coordinate based MLPs benefit -- in terms of preserving high-frequency information -- through the encoding of coordinate positions as an array of Fourier feat…
Robust normalizing flows using Bernstein-type polynomials
Sameera Ramasinghe, Kasun Fernando, Salman Khan +1
Modeling real-world distributions can often be challenging due to sample data that are subjected to perturbations, e.g., instrumentation errors, or added random noise. Since flow m…